University Of California Berkeley

NSF Awards · FY 2025 · 2025-04

A material's force-displacement response, modal response, and wave transmission and absorption response to dynamic loadings, all can be construed as its characteristic fingerprints. The behaviors of materials under dynamic loads that are applied within a fraction of a second remain poorly understood due to the complex, nonlinear interplay between material microstructure, geometry, and applied load. The complexity increases manifold for architected materials, in which topological considerations are paramount to achieve specific responses or functions. Consequently, methodical design of architected materials with optimal dynamic fingerprints is a challenge that has not been adequately addressed. By seamlessly integrating advances in graph network theory, machine learning, numerical simulations, and high-speed additive manufacturing approaches, this Designing Materials to Revolutionize and Engineer our Future (DMREF) award will accelerate the understanding, inverse design, and fabrication of architected materials with tailorable dynamic fingerprints. The outcome will be materials with inversely designed three-dimensional micro-architectures fabricated via desktop additive manufacturing with prescribed behaviors, such as impact shielding and wave transmission. Applications include energy and shock absorption, acoustic wave filtering, stretchable electronics, and other multifunctional material systems. The project will also train graduate and undergraduate students in the new paradigm of autonomous inverse design and additive manufacturing based on desired behaviors. Moreover, demonstration modules, design games, and additive printing activities will be used for outreach to K-12 students. This project will extend graph-based generative machine learning modeling techniques to identify the underlying motifs within architected materials to understand their dynamic behaviors as well as provide an inverse design framework for optimized functional responses. The first step is to develop a graph space model to represent an arbitrary architected material composed of an arbitrarily complex 3D micro-architecture, by size, scale, hierarchy, lattice topology, and material attributes. The next step involves obtaining high-fidelity experimental data and higher-order simulation data with large amounts of lower-order experimental data to accelerate the training and discovery process. A forward graph-based machine learning model will be trained on the combined data for functional response prediction. Lastly, the graph neural network with reinforcement learning will be used to generate graphs with the desired properties based on the forward predictive model. This extensive and experimentally validated framework will be used to discover fundamental knowledge pertaining to structural and dynamic characteristics, which will then be leveraged to inversely design materials with prescribed dynamic fingerprint. This project is co-funded by the Division of Civil, Mechanical and Manufacturing Innovation (CMMI) in the Directorate for Engineering (ENG) and the Division of Information and Intelligent Systems (IIS) in the Directorate for Computer and Information Science and Engineering (CISE). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

The next communication standard “6G” will make use of new spectrum between 7-24 GHz. However, due to incumbent users, the spectrum will need to be shared in a dynamic way, with channels opening up for short durations, requiring agile radios to hop into and out of bands. This motivates the principal investigator to pursue the design of RF front-end radios that can access very large swaths of bandwidth with programmable center frequency and programmable bandwidth, and tunable in a fast and agile fashion using electronic rather than mechanical tuning. This is in stark contrast to how today’s radios work, as they usually cover much smaller bands (less than 1 GHz) and rely on highly selective filters to minimize interference. The proposed radio would provide wideband tunable functionality by making the radio receiver robust against interference. The transmitter will be equally agile and generate as little out-of-band emissions as possible, so that other users can also share the spectrum. Without using explicit filters, this is a great challenge and requires much innovation in the transmitter architecture. Successful realization of the project would enable resilient broadband connectivity for the foreseeable future, thus broadening wireless access, which is a key national priority. The principal investigator plans on training both graduate students and undergraduates who will participate in the project directly and through classroom interactions. The principal investigator will also continue to teach a chip design “tapeout” course giving undergraduates the opportunity to build radios in advanced CMOS technology nodes and also to test their chips in the lab. This will help with workforce training. The principal investigator is proposing a broadband front-end receiver with an electronically tunable filter that automatically tracks the desired channel of operation using mixer-first N-path techniques, up-converting baseband low-pass filtering to a bandpass response. Linearity is preserved by using voltage feedback in the baseband stages. The principal investigator is also proposing a wideband mixed-signal polar power amplifier that can easily adapt to different bandwidths and modulation schemes due to the flexibility afforded by the digital nature of the architecture. To contend with the high levels of quantization noise and image transmissions, a hybrid PA is proposed that incorporates a low power linear PA that combines with the mixed-signal PA, allowing high efficiency, high linearity, and low spurious transmissions. While N-path filters and digital transmitters have received a lot of interest from the research community, the issue of in-band (rather than out-of-band) linearity of the receiver and out-of-band emissions from a transmitter have been mostly ignored. Also, most of the published work has focused on sub-5 GHz radios. Without addressing these key requirements and new frequency bands, these radio architectures cannot be applied to the envisioned new spectrum ranging from 7-20 GHz. The higher frequency bands necessitate radios without mechanical filters (SAW/BAW/F-BAR) with fast switching between sub-bands. The proposal directly addresses these shortcomings using circuit techniques which allow a new generation of programmable, dynamic, and agile front ends to be used for future spectrum allocations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). General audience abstract: Optical atomic clocks are now the most precise and accurate tabletop measurement devices ever constructed by humankind, offering sensitivity to new and exotic physics. The PI has recently developed a new kind of atomic clock apparatus and has used it to demonstrate a comparison between two optical clocks at a precision below one part in 10^19. To give a sense of scale, this corresponds to resolving a difference in the rate the two clocks tick at that would result in them disagreeing with each other by only 1 second after 300 billion years. The PI and a graduate student will use this new apparatus to develop and test ways to use optical atomic clocks to search for dark matter and to detect gravitational waves. This project therefore has the potential to result in new tools for studying the universe through gravitational wave astronomy, and new ways to search for answers to one of the biggest mysteries in physics, the nature of dark matter. The PI will integrate these research topics into new demos and hands-on activities designed to introduce K-12 students to modern physics concepts. Students will engage with these activities at live shows and interactive events as part of the University of Wisconsin “Wonders of Physics” outreach program, with an emphasis on reaching rural communities and Native American reservations in Wisconsin. This project will thereby strengthen public support for modern physics research and help students develop intuition for atomic technologies and their applications. Technical audience abstract: This research project aims to explore and develop emerging applications of optical atomic clocks. The PI has recently demonstrated a first-of-its-kind “multiplexed" optical lattice clock apparatus that enables differential clock comparisons between two or more spatially resolved ensembles of strontium atoms within the same vacuum chamber. These differential measurements eliminate the detrimental effects of clock laser noise and common mode environmental fluctuations, pushing the limits of achievable clock stability and atom-atom coherence. Record differential clock stabilities and fractional frequency precision have now been demonstrated in this apparatus, with a clear path to further gains in performance. The PI and collaborators will use this multiplexed optical lattice clock to develop and demonstrate novel measurement sequences and data analysis techniques for future gravitational wave detection with space-based optical lattice clocks, including the blind injection of simulated gravitational wave signals at realistic strengths. The PI and collaborators will also use the multiplexed optical lattice clock to search for foggy dark matter in previously unexplored regions of parameter space, and to develop new techniques to search for other forms of dark matter. The PI will work with collaborators to develop interactive and engaging demos and inquiry-based activities to introduce K-12 students to modern physics concepts, including the basic principles of atomic clocks and their applications, and will assess their effectiveness using surveys. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-03

PROJECT SUMMARY/ABSTRACT Wildfires are a common occurrence in the western United States, increasing in both intensity and number of acres burned over the past few decades as a result of climate change. The effects of this changing environmental landscape are a critical public health concern. Wildfires pose both acute and chronic health threats. In addition to mortality, exposure to wildfire smoke has been correlated with morbidities such as cancer, cardiovascular disease, and respiratory syndromes. As the risk and extent of wildfires continues to increase, more research is needed to identify biomarkers of exposure, longitudinal health outcomes, and adaptation in populations who are exposed. Epigenetic modifications to DNA have emerged as biomarkers of exposure, health and disease, and are a focus of this new proposal, as well as future work that will be possible through new data collection as described in this R21 application. DNA methylation, one type of epigenetic modification, does not change the underlying sequence, but can alter genome expression. We will utilize a large multi-ethnic cohort of approximately 5,000 adults recruited from the East Bay area in northern California for whom biospecimens and health data were collected at two time points during 2020-2021 when numerous wildfires occurred across the Western US. We will pursue study Aims that address critical barriers to understanding the relationship between exposure to wildfire smoke, epigenetic changes and health outcomes, through the additional collection of comprehensive data to significantly expand the utility of this unique research resource. Specifically, we will: 1) Develop comprehensive wildfire exposure assignments for all cohort members; 2) Collect additional health outcomes, lifestyle and exposure data from all cohort members; and 3) Perform a pilot study of 400 cohort members to evaluate the relationship between exposure to wildfire smoke and DNA methylation, including epigenetic estimates of biological age. We hypothesize that exposure to wildfire smoke is associated with changes to the DNA methylome. Further, we hypothesize that exposure to wildfire smoke is associated with a greater epigenetic age. A comprehensive and integrated approach to particulate matter- associated changes in DNA methylation could help provide the rationale for intervention strategies to reduce health risks, especially in susceptible individuals, with a significant impact on public health. Epigenetic mechanisms are thought to have a central role, not only as relevant elements of pathogenic mechanisms, but can be considered as mediators of the body adaptation to environmental stimuli, such as air pollutants from exposure to wildfires.

NSF Awards · FY 2025 · 2025-03

The natural world is filled with information that spans all of our senses. When we walk through a park we not only see a rich visual world, we also hear sounds ranging from birds tweeting to the conversations taking place around us. All of this information has meaning, informing us about the structure of our current world, how it is likely to immediately change, and how we might best respond adaptively to it. Sounds are an important source of information for daily life. Sounds alert us to danger, they allow us to navigate to targets that cannot be seen, and they help us to know where we are in the environment. To use this information, the brain must both correctly identify sounds and integrate them with other sources of information to support ongoing cognitive functioning. The proposed work seeks to understand how the brain extracts meaning from sound and how the process is integrated with those used to extract meaning from other senses, primarily vision. The project also aims to provide advanced training to both graduate students and post-doctoral scholars in cutting-edge empirical, analytical, and modelling approaches. In detail, the project aims to test an hypothesis about how meaning is extracted from sounds using fMRI. The auditory semantic alignment hypothesis proposes that a set of cortical areas accomplish this extraction through a dual-specialization for auditory processing and particular categories of sounds. These areas are proposed to extract particular types of information from certain categories of sound and pass that information onto a broader semantic network that is amodal, integrating over the senses. Evidence for a similar system has been observed in visual processing. If a homologous structure is observed for auditory processing, it would suggest a general mechanism for extracting meaning from external stimuli. The proposed work has the potential to significantly improve our understanding of how our cortical responses support our perception and adaptation to a dynamic natural world. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

Predicting the responses of ecological communities to rapid environmental change is becoming increasingly important. Making good predictions is challenging because of the potential complexity of interactions among large numbers of species. Additionally, developing robust theory requires well-studied test cases (datasets). While such test cases exist in several long-term forest study systems, there is much less knowledge available for other biomes such as the alpine. The alpine biome is one of the most vulnerable to ongoing climate changes. This project will conduct long-term monitoring of plant community dynamics in the Colorado alpine biome, assembling a unique long-term record of population and community change during a period of rapid environmental change. The data will be usable to advance ecological theory in general. The project will support five undergraduate research students, as well as a postdoctoral researcher and a graduate student. The project will also host two training workshops on these themes for the research community. Data will be integrated into forecasting challenges hosted by the Ecological Forecasting Initiative. The project will also engage >1000 people/year through the Rocky Mountain Biological Laboratory’s visitor center and tour program. Specifically, the project will focus on integrating species-scale demographic models with community-scale models. The project will test hypotheses related to three core questions: 1. What are the roles of spatial neighborhoods, metacommunity/dispersal processes, and ontogenetic/size structure in community assembly? 2. How do these processes influence community dynamics in a variable environment? 3. Can community states in the future be forecasted, and with what uncertainty? The project uses a long-term alpine tundra study system (at 3500 m elevation in the Rocky Mountains). Two datasets will be collected across 100 vegetation plots: (a) annual whole-community demography (survival, size change (growth), fecundity, recruitment) for all individuals including seedlings of all 20 co-occurring species, including georeferencing; and (b) annual remotely sensed multispectral imagery of all plots, obtained during peak flowering, that can be used to increase dataset size via machine learning. To address (Q1), the project will carry out additional seed bank and seed rain studies, then use these data and the Core Data to test hypotheses about species interactions. To address (Q2), the project will build integral projection models and individual based models for all species taking into account density dependence, species interactions, and temporally variable biotic and abiotic context. To address (Q3), the project will then provide falsifiable forecasts with uncertainties for community dynamics under multiple climate change scenarios. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-02

Project summary/Abstract: Pseudomonas aeruginosa and Staphylococcus aureus are the two most frequent causes of sight- threatening bacterial keratitis in the USA. Infections caused by either pathogen can be difficult to treat, complicated by the ever-evolving problem of antibiotic resistance. Even successful treatment does not always prevent scarring and associated vision loss. Importantly, the healthy cornea is infection resistant, able of avoid adhesion by virtually all pathogens, including large inocula of P. aeruginosa or S. aureus. For over three decades, our lab has focused on understanding this intrinsic resistance to infection, and how its compromise by predisposing factors sets up conditions for infection initiation, focusing on both the cornea and the bacteria. The outcomes have contributed substantially to our knowledge about those topics. The cornea is endowed with a high density of sensory nerve endings, including some polymodal by virtue of their Transient Receptor Potential (TRP) cation channels. These are mostly TRPV1 (Vanilloid), with TRPA1 (Ankyrin) present only on a subset of TRPV1 expressing nerves. Recently, we reported that corneal nerves can modulate the healthy cornea’s susceptibility to bacterial adhesion involving TRPV1 and TRPA1. Our new data show that these two receptor types confer selectivity for pathogen type, with only TRPV1 countering S. aureus adhesion, and TRPA1 instead countering P. aeruginosa - only the latter requiring nerve firing. We further report that while inoculation with either pathogen elicits a TRPV1/A1 and nerve dependent immune cell response, the details differ. P. aeruginosa increases numbers of CD45+ and CD11c+ cells, the CD11c+ cell fraction shifting to being less spherical and closer to the corneal surface. Importantly, the CD11c+ cells contribute to countering bacterial adhesion. S. aureus causes a smaller CD45+ cell response, with no increase to CD11c+ cell numbers, and different changes to immune cell morphology and location - including a shift away from the corneal surface. Here, we aim to better understand how TRPV1 and TRPA1 differentially counter S. aureus and P. aeruginosa adhesion to the healthy murine cornea. Thus, we will compare them more thoroughly for contributions of immune cells (aim 1), direct-acting players at the ocular surface (aim 2), and roles of bacterial ligands (aim 3). The approach will include use of multiple state-of-the-art technologies including flow cytometry in aim 1, and high-resolution mass spectrometry and snRNAseq in aim 2. All three aims have potential to yield novel strategies for preventing infection before pathology and the associated risk of vision loss begins. Meanwhile, they will advance our general understanding of the healthy cornea’s response to microbial challenge, the relationship to neuroinflammation, roles of nerves, TRPA1 and TRPV1 in the cornea, and the biology of the ocular surface and tear fluid.

NSF Awards · FY 2025 · 2025-02

This project will work with California coastal communities to produce research and adaptation strategies that address resiliency in the face of increasing coastal flooding, storm intensity, and storm frequency. As seas rise and storms become more intense and frequent, coastal communities will see increasing disruption and loss, which will force adaptation or relocation. To lessen potential destruction and to help prepare communities for hazards, local resiliency measures need to be improved across America’s coastal regions. Creating partnerships among scientists, community groups, and policymakers will help thoroughly investigate and understand possible vulnerabilities and foster the creation and implementation of adaptation strategies that fully account for local needs. This project will increase the collective capacity of a network of ten community and community-based partners, and other actors involved with coastal adaptation. The early-career research team will simultaneously build community capacity and provide support to local and regional adaptation planning efforts currently underway by integrating cutting-edge modeling. Tools, strategies, and lessons learned from this research will be transferable to other regions. Working with two California coastal communities, Belle Haven/East Palo Alto and the Canal District, researchers will develop scenarios for future shorelines, identify knowledge gaps, and evaluate technical assistance tools and programs. The proposed work addresses urgent, necessary challenges of coastal adaptation through the merging of coastal Earth systems models, social science research into governance processes, and the knowledge and perspectives of frontline communities. This project represents a shift in coastal Earth systems model development and application by focusing on including local knowledge and perspectives. State-of-the-art Earth systems modeling will be connected to local and regional decision-making and planning for coastal climate adaptation. This project is jointly funded by the Division of Research, Innovation, Synergies, and Education in the Directorate for Geosciences and the Office of Advanced Cyberinfrastructure through the National Discovery Cloud for Climate initiative. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

The workshop "Advancements in Experimental Design in the Era of AI" will be held from March 7-9, 2025, at the UC Berkeley campus Alumni House. This workshop aims to unite experts from various disciplines to develop and discuss recent advancements in experimental designs that combine classical approaches with cutting-edge artificial intelligence (AI)-driven techniques. In today's data-driven world, understanding cause-and-effect relationships is essential across fields such as healthcare, social policy, and industry, and randomized experiments serve as the gold standard for establishing causal relationships by systematically testing the effectiveness of treatments, interventions, or policies. Well-designed experiments not only yield reliable insights but also reduce costs, accelerate outcomes, and enhance public benefits. However, designing experiments that meet the complex needs of different fields, each with unique challenges and data requirements, is a significant task. While traditional experimental designs have been successful for decades, recent advancements in data collection and AI potentially offer new opportunities to enhance experimental efficiency and insights. This workshop aims to foster collaboration among researchers across different fields to create new experimental design strategies suitable for today's complex data environments. The workshop aims to address the need for a unified approach to experimental design by bridging classical design of experiments (DoE) and modern adaptive methodologies, including reinforcement learning and AI-assisted designs. Classical DoE has been foundational in manufacturing, engineering, and quality control, emphasizing optimized balance and limited sample sizes. However, recent applications in clinical trials and digital platforms may require more adaptive approaches that dynamically adjust based on accruing data. These modern adaptive strategies — such as response-adaptive randomization, enrichment designs, micro-randomization, and multi-arm bandits — offer enhanced statistical efficiency and personalization but necessitate tailored statistical frameworks and causal inference methods. Despite their potential, the application of modern designs has been hindered by limited cross-disciplinary dialogue and implementation guidance. This workshop will convene experts in statistical design, biostatistics, econometrics, political science, and industry to foster interdisciplinary innovation in experimental methodologies. Objectives include fostering knowledge exchange across fields, advancing the integration of adaptive and classical designs, and applying AI tools to optimize experimental processes. By addressing practical challenges and promoting collaboration, the workshop aims to advance experimental design theory and practice, leveraging AI to tackle the complex data landscapes of modern research and industry applications. For more information, please visit the workshop website at: https://www.design-ai.site/Berkeley-2025/ . This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

The global biodiversity crisis is a central problem facing humanity, yet we lack the means to assess biodiversity at the pace of the Earth's changing environment. For this project, rapid assessment technologies will be integrated at the landscape and island level to forecast unseen change in high-impact insect and spider populations tracked by their DNA. The project goal is to infer processes that shape biodiversity and its decline, and how these processes might be captured remotely across different scales and degrees of human impact. In the Hawaiian Islands, the velocity and extent of non-native plant invasions in ecological landscapes will be measured using satellite, helicopter, drone, and ground-based monitoring systems. Those metrics will be combined with assessments of insect biodiversity at the same sites generated from rapid, high-throughput environmental genomic analyses. Outcomes will aid land managers with actionable solutions, building on the ongoing activities of the research team and working with the Pacific Regional Invasive Species and Climate Change (Pacific RISCC) Management Network. Results will be translated for the general public through the web-based ESRI ArcGIS StoryMap. The researchers will provide mentoring for undergraduates, graduate students and a postdoc at the University of California Berkeley, the University of Maryland, and the University of Hawaii Hilo, the latter of which is a primarily undergraduate serving institution that helps meet the needs of Pacific Islanders. Products will include the development of a learning module and toolkit for students to adopt new skills of data analysis and visualization for communicating biodiversity and remote sensing data. Using the model system of the Hawaiian Islands, the project will couple high-throughput arthropod biodiversity sequencing and remote sensing imagery to examine correlated shifts across two orthogonal gradients set within the same native forest type. The first gradient is a geological chronosequence, from 0-5 million years, across which arthropod communities increase in diversity and become more ecologically specialized. The second, intersecting, gradient is composed of a landscape matrix that runs from native to heavily invaded forest habitats on each island. At plot scales, whole arthropod communities will be sampled using genetic signatures from high-throughput sequencing to test models of community assembly over extended ecological-to-evolutionary time, and hence infer the changing roles of key processes of filtering, competition, and neutrality, through time. The models will predict trajectories of disassembly in the face of rapid biotic change. Arthropod community analyses will be coupled with remote sensing imagery at scales ranging from regional (archipelago; satellites), to area (leeward slope of one mountain; helicopter), to plots within heterogeneous landscapes (drone imagery and airborne and ground lidar). The different remote indicators of change in the ecosystem (spectral properties, leaf and water content, nitrogen content, plant stress) will be integrated by using structural equation models (SEMs) to identify candidate parameters that reflect arthropod community dynamics in rapidly changing island forest systems. Joint species distribution models will be used to integrate data across scales. This research will test the predictability of remote sensing data for explaining the spatio-temporal variability of biodiversity and its resilience to anthropogenic modification. In addition to training at the undergraduate, graduate and postdoctoral levels, products will include the development of a learning module and toolkit for students to adopt new skills of data analysis and visualization for communicating biodiversity and remote sensing data. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

Enormous health inequality persists in the United States. Even prior to the COVID-19 pandemic. In many areas of the country, people with a higher income live up to decade longer than those in the lowest income levels. Additionally, the pandemic itself has hit low income and under-served populations especially hard. Biased medical decision-making contributes to this health inequality. For example, previous work has shown that one of widely used health risk prediction algorithms assesses African-American patients as less sick than equivalently sick White patients. This research will make medical decision-making fairer by statistically analyzing the decisions made both by humans and by algorithms. The research will identify sources of bias (for example, when medical tests are given to patients with better access to healthcare rather than to patients most likely to have a disease), and propose solutions (for example, reallocating tests to patients who are predicted to have the highest disease risk). This will not only make healthcare fairer; it can also make it more efficient, by allocating medical resources where they will do the most good. The project will also create a publicly available class on how to design fair algorithms, and conduct a large-scale study of how engineers can be trained to design fairer algorithms, to improve the preparedness of the engineering workforce. Because important medical decisions are made both by humans and by algorithms, the research pursues three objectives: 1) detecting bias in human medical decision-making, focusing on three high-stakes medical settings: allocation of medical testing, healthcare quality assessment, and interpretation of medical images. Further, the project will also build algorithmic decision-aids to reduce human bias, by drawing clinicians’ attention to medically relevant features they may have overlooked. Finally, the project targets making algorithmic decision-making more equitable, by examining the features it is appropriate to include in a medical algorithm. The research will be conducted in collaboration with clinicians to maximize translational benefit to patients. The methods developed, which draw on techniques in Bayesian inference and deep learning to provide interpretable models of how bias arises, are more generally applicable to decision-making across a host of high-stakes domains—including lending and hiring—and thus can impact a wide range of fields concerned with equity in decision-making, including law and economics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-01

PROJECT SUMMARY/ABSTRACT People who lose their central vision due to macular disease such as age-related macular degeneration (AMD) must use their residual peripheral vision for visual tasks. Following the onset of central vision loss, most patients eventually adopt an eccentric retinal location outside the affected macular area, the preferred retinal locus (PRL), as their new reference locus for visual tasks. Little is known as to how and why a location eventually evolves into the PRL. In this project, we are going to tackle the what, where, why, how, who and when of the PRL development. The long-term goals of this project are to understand the mechanism(s) underlying the development of the PRL, and the limiting factors and potentialities of the PRL(s) for various visual tasks so that effective visual rehabilitative strategies can be developed for patients with central vision loss. The first aim of the proposed research addresses the what question. We hypothesize that there is a shift in the origin of the retinotopic reference frame from the fovea to the PRL for people with central vision loss. We will determine the retinal locus used to perform oculomotor and perceptual tasks to evaluate if the retinal locus for these tasks is shifted to a consistent location outside the scotoma with properties similar to those of the fovea. The second aim addresses the where and why questions. We hypothesize that the PRL is the location with the most optimal retinal structures/functions. Optical coherence tomography and adaptive-optics imaging will be used to measure structural properties; and acuity and sensitivity will be measured around the scotoma of participants with central vision loss. We will determine if these structural properties can predict the PRL location. The third aim addresses the how question. We will test the hypothesis that vision at the PRL can be optimized for people with central vision loss. We will measure wavefront aberrations to determine the best spectacle prescription that corrects for optical aberrations to test if this will improve visual performance for our participants. We will also devise a perceptual learning and a saccade training task to determine the effectiveness of these training regimes on improving the functional vision of our participants. The fourth aim addresses the who and when questions. We will track the changes (if any) of the structural properties in the macular region over a period of three years in a cohort of participants with early AMD who do not yet have a PRL, with the expectation that a subset of these participants will eventually develop a PRL, thus allowing us to identify who will develop a PRL and the time-course of the PRL development. Findings from this project will tell us more about the PRL — what it really represents, where it is, why a particular location evolves into the PRL, how vision can be improved or enhanced at the PRL, who will develop a PRL and the time-course of the development (when). The information combined have the potential of developing into useful rehabilitative tools that might help us predict the best location for a PRL or improve the functional vision of people with central vision loss.

NSF Awards · FY 2025 · 2025-01

Non-technical description: Exotic magnetic materials hold great promise for next-generation devices that leverage the spin of the electron to store information (so-called spintronic devices). Such devices hold the promise to possess higher storage densities, greater security to external probes, lower power consumption, and faster switching dynamics. Altermagnets are a recently identified class of magnetic material that display highly attractive functional properties that are more traditionally associated with ferromagnets such as iron. These novel materials offer advantages for electrical control and read-out compared to traditional antiferromagnets but still possess the properties that are desirable for ultra-compact, miniaturized devices. With this project, supported by the Solid State and Materials Chemistry Program in NSF’s Division of Materials Research, researchers at the University of California Berkeley synthesize materials that have been proposed as potential altermagnets but not have not yet been realized. Prof. Bediako and his research group investigate these materials to understand how their solid-state structure and compositional variations dictate their magnetic and electronic properties. These research efforts are integrated with education and outreach initiatives that seek to broaden participation in STEM education and scientific research, in particular through the tutoring of incarcerated students at Mount Tamalpais College at San Quentin State Rehabilitation Center in mathematics, science, and computer science. Technical description: Intercalation compounds of transition metal dichalcogenides (TMDs) are a highly tunable platform for designing the magnetic properties of materials for next-generation spintronics. In this class of solids, changing the transition metal dichalcogenide host lattice, the intercalant identity, and intercalation stoichiometry modulates emergent magnetic behavior. The magnetic properties of these intercalation compounds are also highly sensitive to the nature of defects/disorder within the intercalant lattice. Compared to studies of Cr, Mn, Fe, and Co intercalation in TMDs, there are very limited experimental data in the literature on TMD intercalation compounds of vanadium, despite a few recently having been theoretically proposed as a candidate altermagnetic material - a recently proposed magnetic phase classification that has distinctive electronic and spintronic properties that set it apart from conventional antiferromagnets. More broadly, for the family of V-intercalated TMDs, an understanding of how intercalant stoichiometry alters magnetic properties is completely lacking. This knowledge gap presents critical impediments for an intuitive, chemical understanding of how d-electron count, intercalant structure, and magnetic exchange dictate the varied magnetic properties in intercalated TMDs. In turn, this knowledge gap impedes the rational design of magnetic materials with desirable properties (higher operating temperatures, more efficient spin–charge conversion for spintronics, etc.). To address this challenge, researchers at the University of California Berkeley focus on the synthesis, structural characterization of V-intercalated TMDs and elucidation of connections between synthetic conditions, structure, and physical properties. They use a combination of solid-state synthesis, crystal growth, x-ray and electron diffraction, neutron scattering, magnetometry, and electronic transport measurements to unveil the fundamental knowledge needed to synthesize high purity materials and control exotic magnetic behavior. This work deepens the understanding of fundamental structure-property relationships in intercalation compounds and builds a framework for understanding how to manipulate magnetic and electronic phenomena through solid-state synthesis and materials chemistry principles. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

Personalized learning has proven effective in improving students' learning outcomes and is essential for closing the learning gap among students with varying backgrounds and preparation levels. The emergence of advanced Artificial Intelligence (AI) technologies, including generative AI, creates an opportunity for enhancing the effectiveness and quality of personalized learning. Yet, existing tools are not tailored for educational purposes and generate responses that might not be suitable for students' knowledge level, are inaccurate, and/or are not helpful for students' learning. This project will tailor Large Language Models (LLMs) to account for students' current state of knowledge and learning practices, the learning context, and their perception of the helpfulness of the support they have received in prior interactions with the system. The research will advance the state-of-the-art in modeling students' problem-solving strategies and algorithmic thinking in computer science education. Through implementing the techniques in existing intelligent learning environments, classroom studies, and outreach work, thousands of students at different levels will be able to benefit from these tools, improving their programming knowledge and skills and broadening participation in computing fields. The recent wide availability of LLMs has incentivized different disciplines, including education, to improve existing processes and practices. One key area that is actively being studied is how to tailor conversations toward maximized alignment with user preferences for optimized task completion. In education, this alignment comes from offering adaptive instructional support by modeling students' knowledge state and competencies. This project will develop and evaluate novel AI-based methods for student modeling to trace students' competencies within a range of abstraction levels through (1) integrating fine-grained process data to model students' competencies related to problem-solving strategies, (2) identifying effective and harmful learning patterns, and (3) understanding the consequences of learners' patterns of interactions with the intelligent learning systems on their competence. The project team will use these findings to develop LLM-based systems for generating learning scaffolds -- feedback, worked examples, and suggested next problems -- using a dual-strategy approach that combines the fine-tuning of LLMs with advanced Reinforcement Learning with Human Feedback (RLHF). The goal is that the learning scaffolds generated by the fine-tuned LLM plus RLHF-based agent are more pedagogically relevant for the learner than scaffolds generated by other state-of-the-art models. Output quality will be assessed on three main factors: relevance to classroom content, current competency of the student, and helpfulness of the response from a pedagogical standpoint. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-01

PROJECT SUMMARY Autophagy is universal in eukaryotes as a cytoprotective mechanism for the clearance of inclusions, damaged organelles, and other harmful materials. Autophagy has major roles in aging and neurodegeneration, including in Parkinson's disease, Alzheimer's disease, amyotrophic lateral sclerosis (ALS), frontotemporal degeneration, and Huntington's disease. Autophagy involves the formation of a unique cup-shaped double membrane, the phagophore, which expands, engulfs cargo, and finally closes and fuses with lysosomes. The importance of autophagy as a neuroprotective mechanism has motivated attempts to therapeutically enhance autophagy in the brain. In contrast to nearly all other tissues, bulk autophagy in neurons is decoupled from amino acid levels and mTORC1 signaling. Growing evidence points to levels of the lipid phosphatidylinositol 3-phosphate (PI(3)P) as rate-limiting in autophagy in neurons. Our laboratory has done much of the paradigm shaping work on the molecular, structural, and biochemical mechanisms of regulation of the class III PI 3-kinase complexes that generate PI(3)P, positive and negative regulators of PI 3-kinase complexes, and their PI(3)P-dependent downstream effectors in autophagy. Here, we will combine cryo-EM, structural modeling, biochemical reconstitution, and functional assays and cell imaging in i3Neurons to determine how PI(3)P levels are controlled, and in turn control neuronal autophagy. Assays of i3Neurons will differentiate between bulk autophagy and different forms of mitophagy, and further differentiate between autophagy events in axons, axonal boutons, and soma. Imaging tools will include mitoKeima and HALO-based assays and CLEM and FIB- SEM imaging of autophagy in axons and axonal boutons in i3Neurons bearing a range of knockouts, autophagy probes, and fluorescently tagged subunits of autophagy core complexes. We will determine how the key lipid kinase complex of autophagy, the class III PI 3-kinase complexes I (PI3KC3-C1) is regulated by the key autophagy-initiating ULK1 complex. We will determine the high-resolution structure of the PI3KC3- C1:ULK1C supercomplex and analyze its function and regulation in distinct compartments within neurons.

NSF Awards · FY 2025 · 2025-01

Educators strive to incorporate meaningful and translatable skills that both engage their students in current research and prepare them for careers in microbiome science. In an effort to support instructors, the Microbiome Workforce Development program was established using modular, reproducible scientific method-based training materials designed to be readily incorporated into existing microbiology and bioinformatics courses. Undergraduate educators and collaborating programs developed these modules and training materials to overcome commonly identified barriers and challenges to teaching microbiome research skills across a range of institution types, specifically addressing resource access and affordability, instructor training, effort to implement new concepts, and student engagement. The Microbiomes in Computational Research Opportunities Network (MICROnet) RCN-UBE is focused on facilitating the growth and diversification of an educator network to incorporate MICROnet resources at a range of institutions, build regional networks to support and sustain growth, and evaluate program modules for continued improvement. Supporting instructors at diverse institutions makes for more inclusive training of the next generation of researchers capable of answering current questions and generating open data to address future grand challenges. MICROnet will enable a greater distribution of Microbiome Workforce Development materials through regional networks of instructors and institutions implementing the modules. In building regional hubs, the network will form an inclusive, robust, self-sustaining community of educators that provide peer-led training, resource distribution, and opportunities for local collaboration across institutions. With broader use of the program, MICROnet will assess all five scientific method-based modules: Research Question and Hypothesis Development, Experimental Design and Sample Metadata, Sample Collection and Processing, Data Analysis, and Conclusions and Publishing. Evaluation focuses on accessibility and feasibility of use across a range of institutions, as well as student interest and engagement. Feedback from participants, students and educators, enables the program to evolve with community needs through an iterative development of materials and training, while ensuring MICROnet provides an equitable approach to teaching microbiome science. This project is being jointly funded by the Directorate for Biological Sciences, Division of Biological Infrastructure, and the Directorate for STEM Education, Division of Undergraduate Education as part of their efforts to address the challenges posed in Vision and Change in Undergraduate Biology Education: A Call to Action (http://visionandchange/finalreport/). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

This project aims to revolutionize the field of microwave circuit design by developing an advanced inverse design (InvDes) framework. Microwave circuits are essential in communication and sensing systems critical for consumer electronics, healthcare, and defense, and their design significantly impacts the overall performance, cost, and efficiency of these systems. Current design methods primarily rely on human engineers leveraging their training and experience to craft circuit layouts. In contrast, InvDes lets a computer algorithm find designs that maximize desired performance within specific constraints, discovering new topologies and shapes beyond human intuition. This project will create a systematic framework for the InvDes of planar microwave devices -- filters, splitters, baluns, antennas, and more -- meeting complex performance requirements and shape/size or fabrication constraints demanded by diverse application. The project will support the education of the nation's next generation of electronic engineers, preparing them to excel in the era of advanced computing by involving them in cutting-edge research and developing new curriculum modules. The designs generated by the proposed approach will be fabricated and validated alongside industry partners, solving outstanding challenges in microwave engineering and immediately benefitting a wide array of applications from autonomous vehicle radars to next-generation wireless communication. This project seeks to create a unified framework for the inverse design (InvDes) of planar microwave and millimeter wave devices, moving beyond conventional circuit design techniques to explore a substantially broader design space. Realizing this goal requires parameterization techniques that give the algorithm maximal freedom to design devices of any necessary topology and shape, powerful optimizers pioneered for use in machine learning tasks, and highly efficient simulators such as GPU-accelerated finite difference methods and versatile finite element and boundary element solvers. For the first time, multilayer devices will be fully machine designed, with both metal layers and via placement controlled by the algorithm, enabling InvDes of the full range of modern microwave components. To address the non-convexity of the design landscape, which generally demands many trials with random initial conditions to find high-performing devices, our initializations will be pre-optimized by first minimizing a convex dual problem with relaxed physics. The designs generated by this algorithm will be fabricated using both macroscopic printed circuit board (PCB) and nanoscale complementary metal-oxide semiconductor (CMOS) technology. Experimental verification of device performance will utilize broadband network analyzers, as well as microwave impedance microscopy which permits mapping of the local electric field distribution in operando with exquisite spatial resolution. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

This I-Corps Hubs project develops the infrastructure needed for entrepreneurial training for academic science, technology, engineering, and math (STEM) researchers and high potential community teams. This training will accelerate the commercialization of cutting-edge technologies and enhance regional innovation. It will also support workforce readiness in a region that is rapidly changing as the result of post-pandemic, economic, and geographic dynamics of “meta cities,” and net in and out migration to and from rural areas throughout the region. In addition, Hub activities will provide the training needed to power other NSF initiatives promoting commercialization and innovation. Developing these entrepreneurial skills for both academic researchers and throughout the region’s workforce amplifies the economic and societal impact of NSF and other basic research while accelerating the growth of startups, providing economic benefit to the region and beyond. This will be accomplished so as to multiply opportunities, increases national competitiveness, and secures an brighter economic future for all. This I-Corps Hubs project is based on the translation of deep technologies into societal and economic impact. The collaboration covers a large geographic area that includes both urban and rural locations. The region shares distinct commonalities between the partners and synergies that may be leveraged to serve a unique population and maximize economic impact throughout the region. The Hub activities will be designed to support regional and national I-Corps training through team expansion, fuel regional and national economic growth, produce actionable entrepreneurial research, and increase participation. The Hub partners share a mission to reduce time and risk associated with translating top research from lab-to-market, while expanding educational and economic opportunity throughout the region. Through education, evidence, and experience, the Hub will drive creation of sustainable, scalable technology-based startups with both regional and national impacts. The Hub will strive to raise awareness of the value of entrepreneurship among science and engineering faculty and students, using a variety of programs designed to meet scientists and engineers at their knowledge and skill level, whether they are curious about the fit of their technology to solve an industry problem or are committed company founders. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

This project will identify bioactive compounds made by mutualistic fungi and how much it costs to make them. Fungi make many chemicals that they use to fight off competitors. However, for fungi that form beneficial mutualisms with trees, fighting their competitors may prevent them from helping their tree hosts. These fungi must trade nutrients with trees to survive, and producing defense chemicals uses up nutrients that could be shared instead. Loblolly Pine and its fungal partners are a widespread and economically important symbiosis in the United States. Understanding how fungi balance defending against competitors and helping their host trees is important to the bioeconomy. The project will also identify new bioactive compounds with the potential for future practical uses. This project also supports research experiences for community college students aiming to transfer to four-year degree programs. This research will use ectomycorrhizal fungi in symbiosis with the host tree Pinus taeda, focusing on fungi in the genus Suillus. To identify the metabolites involved in competition and the ecological scenarios that induce bioactive metabolite induction, host trees colonized by Suillus will be challenged with competitors representing three forms of antagonism: competition over substrate, over nutrients, and against injury. After reliable induction triggers are identified, the research team will induce metabolite expression to quantify 1) the cost of production, 2) the effect of fungal competition on the host plant, and 3) the scenarios leading to mutualistic breakdown. This will be achieved using a combination of nutrient-depletion coupled to metabolomics, transcriptomics, and isotopic tagging to track nutrient transfer. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

Modern applications, be it e-commerce sites, ML serving systems or medical applications place increasingly stringent requirements on the cloud storage systems that underpin them. These systems must offer good performance and scalability, as well as robustness against hardware failures and malicious attacks. Consensus systems, specifically, are used as the root of trust to bootstrap application correctness, and ensure that machines agree on a shared state in spite of failures. Their guarantees rely on the trust model (the set of assumptions about reality) accurately describing the conditions under which the system will operate. This means correctly modeling the network, the types of failures that can arise, as well the number of total possible failures. Unfortunately, existing trust models fail to capture realistic deployment conditions. The project's novelties come from developing new trust models and protocols that explicitly recognise the true, uncertain nature of large scale distributed systems. The project's broader significance and importance are its ability to significantly improve the performance and robustness of consensus systems, and as such of all the systems that depend on them. Production consensus implementations are deployed over networks that are heterogeneous between LAN and WAN, with blips, and subject to attack, misconfiguration or link failures. Replicas in these systems all have a probability of failure, and this failure rate evolves over time. Yet, engineers do not currently have a good way to precisely express these realistic setups as current abstractions are too coarse-grained. They must either over-insure or under-insure, leading to poor performance and unnecessarily high replication factors. This project 1) revisits the network model by eschewing the idea that the network is necessarily fully synchronous/asynchronous, paying particular attention to how protocols recover from blips 2) revisits the failure model and introduces probability-native consensus protocols that view failure rates as dynamically evolving probability distributions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-12

The three-year renewal REU Site: Berkeley Engineering Research Experiences for Teachers focused on Artificial Intelligence Resources for a Cleaner Environment (BERETAIRE) is hosted by the University of California-Berkeley. Teams of undergraduate STEM majors, high school teachers and community college educators will collaborate to uncover, understand, and utilize AI for environmental-related research. The power of Artificial Intelligence (AI) to change the way we interact with the world is visible around us every day. This includes routine internet searches for information, teaching and learning in elementary through graduate school settings, and basic and applied research at the frontiers of discovery across all disciplinary fields. This project immerses science and math teachers from local high schools and community colleges in a variety of mechanical, civil and environmental engineering laboratories using AI tools. BERETAIRE will recruit science and math teachers from a broad range of schools in the San Francisco area. Participants will educate, prepare, and encourage their high school and community college students to pursue STEM majors and careers. The three-year REU Site: Berkeley Engineering Research Experiences for Teachers focused on Artificial Intelligence Resources for a Cleaner Environment (BERETAIRE) is hosted by the University of California-Berkeley. This project focuses on research to uncover, understand, and utilize artificial intelligence (AI) resources for environmental sustainability research studies in a variety of engineering laboratories. BERET-AIRE will establish teams of pre-service teachers enrolled in the UC Berkeley CalTeach secondary teacher education program, in-service high school and community college teachers serving broad populations of students in the San Francisco Bay Area. These participants will develop essential research and computational skills using AI resources to complete engineering research projects. One area where AI is particularly powerful is finding features and connections in datasets that cannot be determined utilizing traditional approaches. This can lead to a wide variety of beneficial uses, from enhanced voice recognition and caption generation to benefit the hearing impaired, to discovering new medicine compounds. Teacher teams will also participate in a sequence of summer and academic-year professional development activities that support them to translate their research into classroom instruction. Each RET participant will develop curricula that connects current research to high school and community college science and math learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-12

This project aims to enable upper atmospheric investigations by operating the red- and green-line imagers and Fabry-Perot Interferometers (FPIs) in the existing Mid-latitude All-sky-imaging Network for Geophysical Observation (MANGO) network to collect data in the current solar cycle. The MANGO network was developed with the support of the National Science Foundation (NSF) that observes the light from the airglow and aurora at night across the continental United States. The earth’s upper atmosphere receives energy and momentum inputs from above and below, which manifest in the form of traveling atmospheric/ionospheric disturbances in the thermosphere-ionosphere region. The MANGO observations allow us to understand what energetic events in the lower atmosphere and the sun impact the upper atmosphere over the United States and how. The MANGO observes the low-latitude aurora and waves, and measures the winds and temperature in the upper atmosphere. The data from these observations is made available in near-real time for scientific and public use. This project will continue to operate and maintain the 19 instruments that make up the MANGO network – 15 all-sky imagers and 4 FPIs, process and share the generated data, create higher-level data products, and interface with the scientific community to make progress in understanding the earth’s space weather. Broader impacts of the project include open curated datasets with no embargo period, an open-source software repository maintained on GitHub, and training the next generation of scientists (post-docs and undergraduate students). This five-year project is a collaboration between SRI, University of Illinois Urbana-Champaign, and the University of California, Berkeley. Under this project, the team will operate and maintain the MANGO network established through the NSF Distributed Array of Small Instruments (DASI) program, which includes both red- and green-line all sky imagers (15 at completion) and 4 Fabry-Perot Interferometers, maintain the data infrastructure to collect and share the data to the broader scientific community, create higher-level data products, and interface with the scientists and general public to advance our understanding of the geophysical and geomagnetic processes in the nighttime mid-latitude ionosphere. The MANGO network enables the following scientific investigations: (1) determine spatial scales of the lower and upper thermospheric winds, (2) investigate vertical propagation of thermospheric variability relative to F-region dynamics, and (3) study the relative impact of lower atmospheric forcing with respect to magnetospheric forcing on the mid-latitude thermosphere and ionosphere. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2024-12

PROJECT ABSTRACT Obesity leads to an increased risk for type 2 diabetes, heart attack, hypertension, stroke, and many types of cancer—conditions which are among the leading causes of death in the US. Through twin and family studies, obesity has been found to have a 40-70% heritability rate, pointing to a strong genetic etiology. The long-term objective of our studies is to determine how genetic variation predisposes humans to obesity and the accompanying therapeutic implications. Here we are optimizing the development of a state-of-the-art approach based on CRISPR-mediated gene downregulation in a temporally, anatomically and cell-type specific manner, to rapidly test whether genes in which variation predisposes to obesity are required for the function of specialized neurons in the mouse hypothalamus. We will also be using this approach to test the hypothesis that genes in which mutations cause obesity by affecting the function of the primary cilia, a cell surface projection that receives and transduces select intercellular signals, are required for the function of neurons of central leptin-melanocortin system in mice.

NSF Awards · FY 2024 · 2024-12

The broader impact/commercial potential of this I-Corps project is based on the development of a software tool that integrates semantic information into three-dimensional (3D) models, significantly lowering the barriers to creating visualizations in the architecture, engineering, and construction industries. This tool allows professionals to generate high-quality, contextually relevant visualizations more efficiently, improving the design process by enabling quicker iterations and more accurate project outcomes. By automating and simplifying the generation of these visualizations, the tool reduces the time and cost associated with traditional methods, making advanced design capabilities more accessible to a broader range of professionals, including smaller firms and independent designers. Moreover, the enhanced accessibility and intuitive nature of this tool encourages greater engagement from non-experts, such as community members, in the design process. This increased participation can lead to projects that better reflect the needs and desires of the communities they serve, ultimately resulting in more successful, inclusive, and sustainable designs. This I-Corps project utilizes experiential learning coupled with first-hand investigation of the industry ecosystem to assess the translation potential of the proposed technology. It is based on the prior development of a method for integrating semantic information and text prompts into three-dimensional (3D) models to enhance the visualization and design process. The core innovation lies in the ability to assign semantic data at multiple levels of geometric elements—ranging from textures to entire objects—and to utilize these assignments to inform and refine conditional image synthesis. This approach leverages advancements in machine learning and artificial intelligence (AI) to create visual outputs that are more accurate and more contextually relevant to the specific design scenario. By combining map-based and text-based information, the technology enables a more sophisticated and user-friendly workflow, allowing designers to generate complex visualizations with minimal manual input. The research underpinning this project has demonstrated the feasibility of this approach, showing that it can enhance the efficiency and effectiveness of digital design tools. This innovation has the potential to impact the way architects and engineers approach the visualization phase of design, leading to the development of more advanced automated design systems that can adapt to a wide range of scenarios. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

The broader impact of this I-Corps project is based on the development of a decision-making tool designed to optimize data-driven decision-making in complex systems across various industries. This technology has the potential to enhance operational efficiency, reduce costs, and minimize environmental impact in sectors such as manufacturing and logistics. By enabling organizations to make optimized, real-time decisions, the tool can increase throughput, improve resource utilization, and streamline processes, leading to significant economic benefits. This innovation addresses a critical gap in existing tools, offering a scalable solution that adapts dynamically to real-world complexities. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. The solution is based on the development of a versatile, real-time, data-driven, decision-making application designed to optimize complex systems across various industries. This technology utilizes an advanced simulation and optimization tool that integrates reinforcement learning (RL) and mathematical programming to optimize decision-making in real-time. The core technology simulates complex systems, allowing for the simultaneous optimization of production schedules, workforce allocation, and resource management. Initial research demonstrates that this application can significantly reduce computation time while achieving optimal outcomes, outperforming conventional methods. The technology’s adaptability allows it to be applied across multiple industries, making it a potential solution for optimizing operations in diverse environments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.